Soil copper sources and biogeochemical processes under different land uses: insights from stable copper isotopes in the Cambisols of Southwest China

Study area

The study area is located in the Chenqi watershed, a karst critical zone in Puding County, Southwest China (26°15′16″–26°16′5″N, 105°46′14″–105°46′48″E) (Fig. 1). The Chenqi watershed, covering an area of approximately 1.54 km², is also a long-term observation zone under the purview of the Puding Karst Ecosystem Research Station, Chinese Academy of Sciences (Liu, Han & Zhang, 2020). Th region is characterized by a subtropical monsoon climate, with an average annual temperature of 15 °C and an annual precipitation of 1,315 mm (Zhang & Han, 2023). The typical karst landform in the watershed is characterized by a central depression, encircled by hills on three sides, and an average altitude of approximately 1,350 m (Yue et al., 2020). The calcareous soils found on the hilltops and mountainside are primarily developed from the Middle Triassic Guanling Formation limestone (Zhao et al., 2010). Quaternary sediments located at the foot of the mountain and in the central depression are largely derived from the translocated materials of surrounding hills (Green et al., 2019). The soils, primarily derived from limestone and Quaternary sediments, are predominantly classified as Eutric Cambisols based on the soil classification system approved by the International Union of Soil Sciences (IUSS Working Group, 2022). The area of land covered by Eutric Cambisols accounts for more than 90% of the total area of the Chenqi watershed. The distribution of soil layer is discontinuous, ranging from 10 to 160 cm in thickness with an average of 70 cm (Liu, Han & Zhang, 2020). Puding County encompasses an area of 336 km² dedicated to cropland, constituting 31% of the county’s land area Anshun Municipal Bureau of Statistics, 2025. Over the recent three years, fungicide consumption in agricultural production has averaged 70.15 tons annually, with an application intensity of 208.8 kg km⁻² (Anshun Municipal Bureau of Statistics, 2025).

With population growth and rising food demand, intensive farming practices have led to serious soil degradation over the past 50 years (Liu et al., 2016). In response, the Grain for Green Program (GGP) was implemented to restore the ecological function of these degraded lands (Wang et al., 2017). As a result, many sloped croplands were abandoned and left to evolve naturally. Consequently, the Chenqi watershed currently encompasses a diverse range of land types, including forest, shrubland, grassland, cropland, and abandoned cropland (Fig. 1).

Sample collection

Soil sites were selected from the five distinct land use types, based on the level of agricultural disturbance: cropland (CL), abandoned cropland (AC), abandoned orchard (AO), shrub-grass land (SG), and secondary forest (SF) (Liu et al., 2019). At each site, three soil profiles were excavated, each separated by a distance of 1 meter. The depth of each soil profile was 70 cm. Given the rapid vertical variations in soil properties in surface soils, soil samples were collected with an interval of 10 cm depth at 0–30 cm layer and an interval of 20 cm depth at 30–70 cm layer. To mitigate the effects of soil heterogeneity, three soil samples from one depth layer within a soil site were evenly mixed to form one sample. All soil samples were passed through a two mm sieve and air-dried at room temperature (25 °C). Dominant vegetation near each soil site was collected and freeze-dried for plant samples. Additionally, bedrock samples were collected from the exposed limestone, removing the regolith of the rock surface. Information regarding land use history, soil profile, and dominant plant species for each soil site is shown in Table 1.

Sample analyses

The proportions of soil particle were measured using a laser particle size analyzer (Mastersizer 2000; Malvern, Worcestershire, UK), with a precision of ±1%. Soil organic carbon (SOC) contents were determined using a total carbon analyzer, with a precision of ±0.01%. Soil, plant, and bedrock samples were finely ground (<74 µm) prior to digestion. A strong acid mixture (HNO₃, HCl, HF, and HClO₄) was employed for digestion (Zheng, Han & Liang, 2023). The concentrations of the major elements (including aluminum (Al), calcium (Ca), sodium (Na), potassium (K), iron (Fe), manganese (MN), titanium (Ti)) in the digested solution were determined using Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES, Optima 5300DV; PerkinElmer, Springfield, IL, USA). Cu concentration in the digested solution was determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS, Elan DRC-e; PerkinElmer). To ensure the accuracy of the concentration analyses for all elements, quality control measures, including blank sample and standard sample assessments, were stringently applied. The recovery yields of the digestion procedure for each element exceeded 98.3%, while procedural blank rates remained below 0.3%. Additionally, the relative standard deviation (RSD) for duplicate measurements did not surpass 5%. All element concentrations were measured at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, China.

Analyses of stable Cu isotope ratios

The AG MP-1 anion-exchange resin (100–200 mesh, chloride form; Bio-Rad, Hercules, CA, USA) was packed into a 1.6 mL polypropylene column (BioRad, California, USA). Prior to loading, the resin was washed three times with five ml of seven mol L⁻¹ HCl, 0.001% H₂O₂, and five ml of ultrapure water to equilibrate the resin environment. The digested samples were loaded onto the resin, followed by the addition of seven ml of seven mol L⁻¹ HCl and 0.001% H₂O₂ to elute impurities. Subsequently, the pure Cu solution was steamed dry and then dissolved in diluted HNO₃. The Ni concentration in the sample was tested after separation to ensure the Ni/Cu ratio <0.01, thereby avoiding interference with the stable Cu composition analysis (Ren et al., 2022). The anion exchange technology achieved Cu recovery yields exceeding 95%, with procedural blank rates remaining below 0.2%. All separation processes for the Cu solution were conducted at the Nu Surficial Environment & Hydrological Geochemistry Laboratory (Nu-SEHGL) at the China University of Geosciences (Beijing), China.

Stable Cu isotope ratios were measured using a Multi-collector Inductively Coupled Plasma Mass Spectrometer (MC-ICP-MS) instrument (Nu Plasma III; Nu Instrument, Wrexham, United Kingdom), operated in low-resolution mode at the Nu-SEHGL. To mitigate instrument mass bias, standard-sample bracketing (SSB) and pre-calibrated internal standard solutions were utilized (Zeng & Han, 2020). The stable Cu isotope ratios in soil, plant, and bedrock samples were expressed as δ⁶⁵Cu (‰), as equation following: (1)${\displaystyle {\mathrm{\delta }}^{65}\mathrm{Cu}=\left[\frac{{\left(\frac{65\mathrm{Cu}}{63\mathrm{Cu}}\right)}_{\text{sample}}}{{\left(65\mathrm{Cu}63\mathrm{Cu}\right)}_{\text{ERM-AE}633}}-1\right]\times \mathrm{1000}}$

where ERM-AE633 was used as the standard material, as opposed to NIST-SRM-976 which has been widely used in the past. Unfortunately, the global supply of NIST-SRM-976 is nearly depleted (Moeller et al., 2012), necessitating the switch to ERM-AE633. The measurement sequence incorporated the reference material of the ERM-AE633 (Institute for Reference Materials and Measurements, IRMM, Belgium). The measured δ⁶⁵Cu values of the NIST-SRM-976 were −0.01‰ ± 0.05‰ relative to the ERM-AE633. All δ⁶⁵Cu data are reported normalized to the NIST-SRM976.

Enrichment factor (EF_Cu) of Cu in soil

The enrichment factor (EF) method was employed to calculate the degree of Cu enrichment in the soils. Titanium (Ti) is an element that exhibits resistance to chemical weathering and is minimally impacted by anthropogenic activities (Tessier, Campbell & Bisson, 1979). Thus, Ti is frequently employed as a reference element to indicate the enrichment degree of other heavy metal elements (Sezgin et al., 2003; Yuan et al., 2020). The formula for the calculation of the enrichment factor is as follows: (2)${\displaystyle {EF}_{\mathrm{Cu}}={\left(\mathrm{Cu}/\mathrm{Ti}\right)}_{\text{sample}}/{\left(\mathrm{Cu}/\mathrm{Ti}\right)}_{\text{background}}}$ where (Cu/Ti)_sample refers to the ratio of measured Cu content to Ti content in soil sample, (Cu/Ti)_background represents the average ratio of Cu content to Ti content (mg kg⁻¹) in the Guizhou soil of China (Chen et al., 1991). The EF_Cu < 1 suggests the absence of Cu pollution, 1 ≤ EF_Cu < 2 indicates slight Cu pollution, 2 ≤EF_Cu < 5 indicates moderate Cu pollution, and EF_Cu ≥ 5 denotes significant Cu pollution in soils (Barbieri, 2016).

Chemical index of alteration (CIA)

The chemical index of alteration was employed to evaluate the magnitude of chemical weathering in weathered soils (Kurtz et al., 2000). The formula is presented below: (3)${\displaystyle \mathrm{CIA}=\left[{\mathrm{Al}}_2{\mathrm{O}}_3/\left({\mathrm{Al}}_2{\mathrm{O}}_3+{\mathrm{CaO}}^{\ast }+{\mathrm{Na}}_2\mathrm{O}+{\mathrm{K}}_2\mathrm{O}\right)\right]\times \mathrm{100}}$ where Al₂O₃, Na₂O, and K₂O (mol kg⁻¹) represent their mole fraction in whole soil and CaO^∗ (mol kg⁻¹) indicates the Ca mole fraction in the silicate component. Higher CIA values are indicative of increased chemical weathering in the soil.

Statistical analyses

A one-way analysis of variance (ANOVA), complemented by Tukey’s HSD test, was employed to discern significant differences in soil Cu contents and δ⁶⁵Cu values across varied land uses. Linear regression analysis was leveraged to examine the relationships between soil δ⁶⁵Cu values and soil physicochemical properties in soil profiles under different land uses. Stepwise regression analysis was used to explore the primary factor affecting soil Cu contents and δ⁶⁵Cu value of Cambisols in the karst region. Additionally, non-linear regression analysis was implemented to explore the relationships between δ⁶⁵Cu values and annual precipitation in different regions. All statistical analyses were executed using the SPSS and SigmaPlot software packages.

Soil physiochemical properties under different land uses

Clay proportions in soils ranged from 14.5% to 24.6%, with an average of 19.6% (Fig. 2A). Soils in secondary forest land and abandoned orchard presented higher clay particles compared to other lands. In secondary forests, clay proportions increased significantly with increasing soil depth, while those exhibited a slight decrease with depth in other land-use types. Silt proportion in soils ranged from 75% to 86%, showing variations along soil profiles that were inverse to those of clay proportions (Fig. 2B). The highest SOC content was observed in secondary forest soils (over 60 g kg⁻¹ in topsoil), whereas in other land-use types, those were below 40 g kg⁻¹, with the lowest average content (8.7 g kg⁻¹) found in cropland (Fig. 2C). Generally, decrease in SOC content with increasing soil depth was noted across all land uses. The Al₂O₃ contents in soils varied between 9.9% and 18.8%, with the highest content in cropland and the lowest content in secondary forest land (Fig. 2D). Besides shrub-grass land, Al₂O₃ contents in other lands increased with increasing soil depth. For all land uses except secondary forest, Fe₂O₃ contents ranged from 6% to 9% and typically exhibited a slight decrease with increasing soil depth (Fig. 2E). However, in secondary forest soils, Fe₂O₃ contents rose significantly from 4.0% to 6.7% with soil depth. MnO contents in shrub-grass land and abandoned cropland and orchard slightly decreased with increasing soil depth, ranging between 0.10% and 0.18% (Fig. 2F).

Secondary forest land soils had the lowest MnO content, which declined from 0.06% to 0.02% with increasing soil depth. In cropland soils, MnO contents decreased from 0.16% to 0.06% within the 0–30 cm layer, but then increased to 0.16% at the profile’s bottom. The CIA values for cropland, abandoned cropland and orchard, and shrub-grass land soils demonstrated a slight uptick with increasing soil depth, ranging from 69 to 76 (Fig. 2G). In contrast, secondary forest land exhibited a more pronounced increase in CIA values, rising significantly from 59 to 72 as soil depth increased. With the exception of cropland soils, the EF_Cu values for other land soils spanned a range of 0.6 to 1.2 and commonly did not show intensive variation with an increase in soil depth (Fig. 2H). However, the EF_Cu values in cropland soils were lower in the top layer (0–20 cm depth) with an average of 1.2, compared to those found below 20 cm depth, which averaged 1.7.

Soil Cu contents and δ⁶⁵Cu values under different land uses

Soil Cu contents in cropland (mean 44.9 mg kg⁻¹) were significantly higher than those in abandoned cropland and orchard (mean 37.7 mg kg⁻¹), and considerably greater than those in the shrub-grass land and secondary forest land (mean 26.3 mg kg⁻¹) (Fig. 3B). Soil Cu contents generally increased with increasing soil depth in cropland and abandoned cropland, while those decrease with soil depth in abandoned orchard (Fig. 3A). Noteworthy is the lack of significant vertical variations in Cu contents under shrub-grass and secondary forest.

Soils in cropland (mean δ⁶⁵Cu: −0.216‰) exhibited a significant depletion in ⁶⁵Cu compared to those in abandoned cropland and orchard (mean δ⁶⁵Cu: 0.020‰) (Fig. 3D). In the secondary forest land and shrub-grass land, soil δ⁶⁵Cu values ranged from 0.338‰ to −0.627‰, with an average of −0.130‰. Notably, the δ⁶⁵Cu values in all lands decreased as soil depth increased (Fig. 3C).

Relationships between soil δ⁶⁵Cu values and soil physicochemical properties

The results of linear regression analysis indicated a significant correlation between soil δ⁶⁵Cu values in secondary forest land and soil physicochemical properties at a significance level of p < 0.05 (Fig. 4). Comparatively, the relationships observed in other land use types were weak or non-significant (i.e., p > 0.05 and R² < 0.6). This was mainly attributed to the limited range of δ⁶⁵Cu values in those profiles, which hindered statistical robustness. Hence, the regression models with a goodness-of-fit (R²) exceeding 0.4 were also trustworthy. Specifically, soil δ⁶⁵Cu values were negatively correlated with clay proportions in secondary forest land, whereas the correlation was positive in cropland (Fig. 4A). Conversely, soil δ⁶⁵Cu values showed a positive correlation with silt proportions in secondary forest land but a negative correlation between them in cropland (Fig. 4B). Additionally, soil δ⁶⁵Cu values were positively correlated with SOC contents in secondary forest land and abandoned orchard (Fig. 4C), while demonstrating a negative correlation with Al₂O₃ contents in the same land types (Fig. 4D). Furthermore, soil δ⁶⁵Cu values were negatively correlated with Fe₂O₃ contents across secondary forest land, cropland, and shrub-grass land (Fig. 4E). Positive correlations were observed between soil δ⁶⁵Cu values and MnO contents in secondary forest land and abandoned orchard but negative correlations between them in cropland and shrub-grass land (Fig. 4F). Finally, soil δ⁶⁵Cu values were found to have negative correlations with CIA values in secondary forest land and abandoned orchard (Fig. 4G), while no significant correlation was identified with EF_Cu values across all lands (Fig. 4H).

The results of the stepwise regression analysis revealed that Fe₂O₃ content, Al₂O₃ content, and EF_Cu value were the principal factors affecting soil Cu content in the karst region (Table 2). Among these, Fe₂O₃ content was the most significant predictor of soil Cu content. In comparison, SOC content and MnO content emerged as the primary determinants of the soil δ⁶⁵Cu value in the region. Notably, SOC content was more important than MnO content for predicting soil δ⁶⁵Cu value.

Identification of soil Cu sources under different land uses

Soil Cu primarily originates from parent materials (or bedrock) and external input (Fekiacova, Cornu & Pichat, 2015). The Chenqi watershed, a typical agricultural watershed distant from mining and smelting areas and urban centers (Liu, Han & Zhang, 2020), relies on limestone, agricultural sources and plant inputs as primary contributors to soil Cu. Soil Cu contents in agricultural lands (including cropland, abandoned cropland and orchard) exceeded the regional background value of 32 mg kg⁻¹ (Chen et al., 1991). Conversely, natural lands (including secondary forest and shrub-grass land) exhibited significantly lower in soil Cu content compared to this background value (Fig. 3B). Similarly, the EF_Cu values were predominantly above 1 in agricultural lands (Fig. 2H), indicating slight Cu pollution in these soils. In comparison, the EF_Cu values in the natural lands were below 1, indicating that their primary source from bedrock and plants. Thus, these findings suggested that external inputs from agricultural activities contributed to soil Cu accumulation in cropland, abandoned cropland and orchard.

Soil δ⁶⁵Cu values did not significantly correlate with EF_Cu values across all lands (Fig. 4H), suggesting that soil Cu dynamics were influenced by multiple sources or processes. To identify the potential soil Cu sources, δ⁶⁵Cu values in plant and bedrock samples near soil sites were analyzed, using them as endmembers (Fig. 5). The δ⁶⁵Cu values of limestone ranged from −0.637‰ to −0.582‰, those closely align with the δ⁶⁵Cu composition (mean −0.608‰) in the C horizon of soil profile under secondary forest (Fig. 5A). This observation corroborates previous findings that δ⁶⁵Cu values in the C horizon often resemble those in bedrock (Zheng et al., 2024). For the plant samples, there were significant differences in δ⁶⁵Cu values between grass (aboveground tissues) and arbor (leaf), with ranges of −0.243‰ to −0.115‰ and −0.863‰ to −0.386‰, respectively (Fig. 5B). Globally, trees exhibit significantly depleted δ⁶⁵Cu values (−1.8‰ to −0.8‰) compared to grasses (−0.2‰ to 0.7‰) (Zheng et al., 2024). Furthermore, δ⁶⁵Cu compositions from agricultural sources, primarily fungicides (−0.49‰ to 0.31‰, Babcsányi et al., 2016) and farmyard manure (0.12‰ to 0.52‰, Fekiacova, Cornu & Pichat, 2015), were included as additional endmembers (Fig. 5A).

The mixing of multiple sources significantly influenced soil δ⁶⁵Cu compositions. To discern the origins of soil Cu, the scatter plot of 1/[Cu] (i.e., reciprocal transformations of soil Cu contents) versus δ⁶⁵Cu for soil samples and other potential sources was extensively employed (Ren et al., 2022; Zheng, Han & Liang, 2023). In the agricultural lands, the soil samples were distributed between the agricultural source endmember and the bedrock endmember (Fig. 5A). Notably, soil samples from cropland were more proximate to the ⁶⁵Cu-depleted fungicides compared to those from abandoned cropland and orchard. This alignment with practical observations can be attributed to the cessation of fungicide application post-abandonment. Crop litter input barely contributed to soil Cu due to the non-return of straw during the harvest season in this area (Liu, Han & Zhang, 2020). Additionally, ⁶⁵Cu-depleted litter with its decomposition typically leads to diminished δ⁶⁵Cu value in the topsoil (Weinstein et al., 2011). Nevertheless, soil δ⁶⁵Cu values remained relatively consistent at depths of 0–30 cm (Fig. 3C), corroborating this hypothesis. In comparison, plant sources showed some contribution to soil Cu in the abandoned cropland and orchard, due to vegetation restoration and litter return. Therefore, the topsoil (0–10 cm layer) exhibited slightly ⁶⁵Cu-depletion relative to the deeper soil at the 10–20 cm depth in both lands (Fig. 3C). Yet, δ⁶⁵Cu values in topsoil (mean 0.015‰) markedly deviated from the plant endmember (mean −0.625‰) (Fig. 5), suggesting that plants contributed minimally to soil Cu in agricultural lands. In the natural lands, the primary sources of soil Cu were unequivocally plants and bedrock. However, both plant and bedrock endmembers displayed ⁶⁵Cu-depletion relative to soil samples, resulting in that soil samples did not distribute between plant and bedrock endmembers (Fig. 5B). This result implied that stale Cu isotope fractionation via biogeochemical processes affected soil δ⁶⁵Cu composition.

Biogeochemical processes of Cu under different land uses

Soil δ⁶⁵Cu compositions are also affected by various biogeochemical processes, encompassing redox reactions, mineral dissolution and precipitation, mineral sorption, organic complexation, and plant uptake (Liu et al., 2014; Maher & Larson, 2007; Ryan et al., 2014; Weinstein et al., 2011). The distribution patterns of δ⁶⁵Cu in soil profiles and their correlations with soil physiochemical properties can indicate the key biogeochemical processes affecting Cu in specific environment (Babcsányi et al., 2016; Fekiacova, Cornu & Pichat, 2015). Dissolved Cu(II) generally enriches the heavier isotope compared to Cu(I)-sulfide minerals during redox reactions (Kimball et al., 2009). Therefore, soil δ⁶⁵Cu values tend to rise significantly with increasing soil depth in the land with frequent redox transitions (Zheng et al., 2024), because ⁶⁵Cu-enriched Cu(II) moves downwards under leaching process. However, the δ⁶⁵Cu values in the profiles of all lands diminished with increasing soil depth (Fig. 3C). Moreover, the Cambisols in the karst region have not undergone frequent redox transitions during their pedogenic processes. Thus, the impact of redox reactions on soil δ⁶⁵Cu composition were not dominant.

The reaction procedure of mineral dissolution is strongly influenced by soil pH and organic ligands (Murphy, Oelkers & Lichtner, 1989). Acidic environments facilitate the dissolution of Cu-containing minerals such as sulfides, carbonates, and hydroxide minerals, resulting in a preferential release of ⁶⁵Cu-enriched Cu(II) into the soil solution (Fekiacova, Cornu & Pichat, 2015). However, Cambisols developed from limestone are weakly alkaline (Liu, Han & Zhang, 2020). Thus, the effect of pH on Cu isotope fractionation during the dissolution of Cu-bearing minerals can be discounted. In comparison, the influence of organic ligands seemingly existed in the organic matter-rich Cambisols. Organic ligands facilitate the release of isotopically heavy Cu into the soil solution through ligand-promoted dissolution, leaving behind ⁶³Cu-enriched residual minerals (Zheng et al., 2024). Therefore, there will be a negative correlation between SOC content and soil δ⁶⁵Cu values if the effect of organic ligands is the primary factor. Nevertheless, soil δ⁶⁵Cu values demonstrated a positive correlation with SOC content across in all lands (Fig. 4C), suggesting that the role of organic ligands on soil δ⁶⁵Cu composition was not dominant. In addition, the precipitation of Cu as carbonates is a prevailing process in calcareous soils (Ponizovsky, Allen & Ackerman, 2007), and may also occur in the Cambisols of this area. Secondary Cu-bearing carbonate minerals tend to be ⁶³Cu-enriched and are prevail in the silt fraction (Babcsányi et al., 2016). However, a notable positive correlation between soil δ⁶⁵Cu values and silt proportions in secondary forest land (Fig. 4B) implies that Cu precipitation as carbonate cannot explain the profile variations in soil δ⁶⁵Cu composition.

The sorption of Cu(II)aq by metal (oxyhydr)oxides and clay minerals significantly influences Cu translocation and isotope fractionation during weathering and pedogenic processes (Pokrovsky et al., 2008). For the chemical weathering of carbonate rocks, although CIA values (59–76) being relatively low in soils (Fig. 2G), enrichment of Fe and Al and production of clay mineral were already occurring at this stage. Isotopically light Cu is preferentially sorbed by clay minerals (e.g., kaolinite) and Mn oxyhydroxides (e.g., birnessite) (Ijichi, Ohno & Sakata, 2018; Li, Liu & Li, 2015; Sherman & Little, 2020), while Fe (oxyhydr)oxides and gibbsite (Al₂O₃.3H₂O) are expected to sorb isotopically heavy Cu (Pokrovsky et al., 2008). The CIA values, Fe₂O₃ contents, and Al₂O₃ contents all exhibited negative correlations with soil δ⁶⁵Cu values (Fig. 4), suggesting that the sorption of Fe (oxyhydr)oxides and gibbsite to isotopically heavy Cu was not dominant processes affecting soil δ⁶⁵Cu composition. Soil δ⁶⁵Cu values demonstrated negative correlations with MnO contents in the cropland and shrub-grass land, whereas positive correlations between them were observed in the secondary forest land and abandoned orchard (Fig. 4F). These results suggested that the sorption of Mn oxyhydroxides to isotopically light Cu significantly affected soil δ⁶⁵Cu composition in cropland and shrub-grass land, but not in secondary forest land and abandoned orchard. Notably, clay fractions encompass both clay minerals and Fe, Al, and Mn (oxyhydr)oxides (Babcsányi et al., 2016). Given the negative correlation between soil δ⁶⁵Cu values and clay proportions in the secondary forest land (Fig. 4A), it can be inferred that soil δ⁶⁵Cu composition is significantly affected by the sorption of clay minerals to isotopically light Cu. Overall, Cu isotope fractionation through mineral sorption affects soil δ⁶⁵Cu variation.

Organic complexation exhibits a stronger ability for Cu retention compared to the sorption of clay minerals (Vialykh, Salahub & Achari, 2019). Generally, isotopically heavy Cu(II) preferentially bonds with the carboxyl and carbonyl O ligands, while ⁶³Cu is enriched the Cu-SOM complexes formed through bonding with amino N ligands (Ryan et al., 2014; Sherman & Little, 2020). Plant fragments and polysaccharides, rich in carboxyl and carbonyl O ligands, are predominantly found in coarser (silt) fractions (Quenea et al., 2009). Silt fractions exceeded 75% (Fig. 2B), suggesting the dominance of carboxyl and carbonyl O ligands. This dominance resulted in a positive correlation between SOC content and soil δ⁶⁵Cu values (Fig. 4C and Table 2). In comparison, amino N-containing compounds tend to accumulate and stabilize in clay fractions (Mertz, Kleber & Jahn, 2005), leading to an ⁶³Cu-enrichment in organo-clay complexes. These ⁶³Cu-enriched Cu-organo-clay complexes, along with clay minerals, migrate downward under leaching process, accounting for decrease in soil δ⁶⁵Cu values with increasing soil depth (Fig. 3C). Moreover, the observed increase in CIA values with soil depth (Fig. 2G) is commonly associated with the downward translocation of secondary minerals in clay fractions due to leaching (Mei et al., 2021), confirming the prevalence of leaching in the study area. Additionally, abandoned cropland and orchard exhibited lower soil Cu contents and higher δ⁶⁵Cu values compared to cropland (Fig. 3B), also attributed to the loss of ⁶³Cu-enriched organo-clay complexes and/or clay minerals through leaching. Thus, Cu isotope fractionation, influenced by organic complexation, clay mineral sorption, and leaching process, primarily shape the δ⁶⁵Cu patterns in soil profile (Fig. 5B).

In addition, Cu isotope fractionation in plant-soil systems can affect soil δ⁶⁵Cu composition (Zheng et al., 2024). In natural lands, plants were significantly ⁶³Cu-enriched compared to soils (Fig. 5B), mainly resulting from the preferential assimilation of isotopically light Cu in soils (Ryan et al., 2013). The ⁶³Cu-enriched litter accumulates on the soil surface, subsequently leading to a decrease in the δ⁶⁵Cu value in topsoil as litter decomposes (Ren et al., 2022). Conversely, there was a slight ⁶⁵Cu-enrichment in topsoil (0–10 cm depth) compared to the deeper soils at the 10–20 cm depth in shrub-grass land and secondary forest land (Fig. 3C). This result is likely attributed to the minimal contribution of plants (which are not copper-accumulating vegetations) to soil Cu via litter return. Therefore, the δ⁶⁵Cu signature derived from ⁶³Cu-enriched plants was overprinted by Cu isotope fractionation in soils.

δ⁶⁵Cu patterns in different regions

This case study underscores the crucial roles of mineral sorption, organic complexation, and leaching process in affecting soil δ⁶⁵Cu patterns. Each of these primary factors is intimately tied to climate conditions. To explore the δ⁶⁵Cu patterns of Cambisols, correlations between their δ⁶⁵Cu values and annual precipitation in different region (Table S1) were analyzed (Fig. 6). In humid climate regions, pedogenic processes predominantly involve the formation of secondary clay minerals, facilitated by abundant rainfall (Mei et al., 2021). This leads to the retention of isotopically light Cu through the sorption of clay minerals (Li, Liu & Li, 2015). Meanwhile, a moist soil environment accelerates the decomposition of ⁶⁵Cu-complexed coarse organic matter (Ryan et al., 2014), leading to the loss of ⁶⁵Cu(II)aq due to leaching. As a result, soil δ⁶⁵Cu values declined with increasing annual precipitation in humid climates. In comparison, in subhumid climate regions, increased in rainfall may boost the formation of Fe (oxyhydr)oxides during pedogenic processes and the accumulation of coarse organic matter as vegetation proliferates (Merino et al., 2021). Both processes favor ⁶⁵Cu retention in soil through mineral sorption and organic complexation (Li, Liu & Li, 2015; Ryan et al., 2014). Therefore, soil δ⁶⁵Cu values rise with increasing annual precipitation in subhumid climates. However, future research should delve into the relationships between soil δ⁶⁵Cu values and other factors, such as bedrock types, soil properties, and vegetation types), to gain a more comprehensive understanding of Cambisol δ⁶⁵Cu pattern and driving factors on a global scale.